Understanding multiple days’ metro travel demand at aggregate level

Duan, Zhengyu; Lei, Zengxiang; Zhang, Michael; Li, Haifeng; Yang, Dongyuan

doi:10.1049/iet-its.2018.5004

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…Based upon time-series intercity mobility flow matrix, we use a rank reduction algorithm to identify the potential intercity mobility patterns. The regular rank reduction algorithms, such as PCA (principal component analysis) [33], ICA (independent component analysis) [34], and SVD (singular value decomposition) [12,20,21] have been widely used to extract a low number of latent components from high-dimensional data. However, traditional rank reduction algorithms can not guarantee the non-negativity of the results, even when the input initial matrix elements are all positive, leading to interpretability issues.…”

Section: Intercity Mobility Pattern Recognitionmentioning

confidence: 99%

“…However, such a method mainly analyzes the intercity patterns from the average level at a certain time or stage and fails to effectively characterize the dynamic variations of urban connection. To deal with this issue, existing studies have used singular value decomposition (SVD) [20,21] to characterize the main intracity and intercity mobility patterns. However, one concern is that the obtained decomposition results are not strictly non-negative, leading to interpretability issues.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Yin

2022

IJGI

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Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to measure intercity mobility characteristics. Next, we calculate the Normalized Levenshtein Distance for Population Mobility Structure (NLPMS) to quantify the differences in intercity mobility structures, and we use the Non-negative Matrix Factorization (NMF) to identify intercity mobility patterns. Our results showed an evident ‘Core-Periphery’ differentiation characterized by intercity mobility, with Guangzhou and Shenzhen as the two core cities. An obvious daily intercity commuting pattern was found between Guangzhou and Foshan, and between Shenzhen and Dongguan cities at working time. This pattern, however, changes during the holidays. This is because people move from the core cities to peripheral cities at the beginning of holidays and return at the end of holidays. This study concludes that Guangzhou and Foshan have formed a relatively stable intercity mobility pattern, and the Shenzhen–Dongguan–Huizhou metropolitan area has been gradually formed.

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Section: Intercity Mobility Pattern Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Yin

2022

IJGI

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“…The use of crowd‐sensed data from the mobile phone for mobility prediction of the vehicles is present in the literature. A dimensionality reduction‐based travel demand model is presented by Duan et al [179] Chu et al . [180].…”

Section: Future Of Crowd Intelligence In Transportation Systemmentioning

confidence: 99%

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Shit

2020

IET Intelligent Transport Systems

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“…Non-negative Matrix Factorization (NMF) based solutions have also been proposed for other problems related to urban mobility, such as predicting road traffic [11,33] and predicting metro traffic demand [6]. These works provide powerful solutions to predict traffic, but lack explanatory power.…”

Section: Related Workmentioning

confidence: 99%

Station-to-User Transfer Learning

Zhang

Pfoser

Züfle

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Urban areas provide us with a treasure trove of available data capturing almost every aspect of a population's life. This work focuses on mobility data and how it will help improve our understanding of urban mobility patterns. Readily available and sizable farecard data captures trips in a public transportation network. However, such data typically lacks temporal signatures and as such the task of inferring trip semantics, station function, and user clustering is quite challenging. While existing approaches either focus on stationlevel or user-level signals only, we propose a Station-to-User (S2U) transfer learning framework, which augments user-level learning with shared temporal patterns learned from station-level signals. Our framework is based on a novel, so-called "Tidal-Regularized Non-negative Matrix Factorization" method, which incorporates a-priori tidal traffic patterns in generic Non-negative Matrix Factorization. To evaluate our model performance, a user clustering stability test based on the classical Rand Index is introduced as a metric to benchmark different unsupervised learning models. Using this metric, quantitative evaluations on three real-world datasets show that S2U outperforms two baselines methods by 7 − 21%. We also provide a qualitative analysis of the user clustering and station functions for the Washington D.C. metro and show how S2U can support spatiotemporal urban analytics.

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Understanding multiple days’ metro travel demand at aggregate level

Cited by 10 publications

References 36 publications

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Station-to-User Transfer Learning

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